Overview

Dataset statistics

Number of variables19
Number of observations6908
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory152.0 B

Variable types

Categorical6
Numeric13

Alerts

track has a high cardinality: 6604 distinct valuesHigh cardinality
artist has a high cardinality: 2021 distinct valuesHigh cardinality
uri has a high cardinality: 6898 distinct valuesHigh cardinality
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 2 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
valence is highly overall correlated with danceability and 1 other fieldsHigh correlation
duration_ms is highly overall correlated with sectionsHigh correlation
sections is highly overall correlated with duration_msHigh correlation
instrumentalness is highly overall correlated with targetHigh correlation
target is highly overall correlated with instrumentalnessHigh correlation
track is uniformly distributedUniform
uri is uniformly distributedUniform
target is uniformly distributedUniform
key has 912 (13.2%) zerosZeros
instrumentalness has 1332 (19.3%) zerosZeros

Reproduction

Analysis started2022-11-29 22:50:09.272776
Analysis finished2022-11-29 22:50:26.600094
Duration17.33 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6604
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
O último romântico
 
5
Dungeon
 
4
Hold Me
 
4
Victory
 
4
Talk To Me
 
4
Other values (6599)
6887 

Length

Max length146
Median length72
Mean length17.900261
Min length2

Characters and Unicode

Total characters123655
Distinct characters175
Distinct categories13 ?
Distinct scripts5 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6347 ?
Unique (%)91.9%

Sample

1st rowWalking Blues
2nd rowSuddenly Last Summer
3rd rowSanctuary
4th rowThe Wild Rover
5th rowIn The Driver's Seat

Common Values

ValueCountFrequency (%)
O último romântico 5
 
0.1%
Dungeon 4
 
0.1%
Hold Me 4
 
0.1%
Victory 4
 
0.1%
Talk To Me 4
 
0.1%
Heartbroke 4
 
0.1%
Call Me 4
 
0.1%
Uncle Pen 4
 
0.1%
Holiday 4
 
0.1%
I Wouldn't Change You If I Could 4
 
0.1%
Other values (6594) 6867
99.4%

Length

2022-11-29T17:50:26.681438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 982
 
4.1%
551
 
2.3%
you 470
 
2.0%
love 412
 
1.7%
of 392
 
1.6%
i 381
 
1.6%
a 344
 
1.4%
me 334
 
1.4%
in 333
 
1.4%
to 279
 
1.2%
Other values (5795) 19612
81.4%

Most occurring characters

ValueCountFrequency (%)
17182
 
13.9%
e 11807
 
9.5%
o 8054
 
6.5%
a 7017
 
5.7%
n 6371
 
5.2%
i 5787
 
4.7%
r 5511
 
4.5%
t 5333
 
4.3%
s 3887
 
3.1%
l 3792
 
3.1%
Other values (165) 48914
39.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79908
64.6%
Uppercase Letter 22142
 
17.9%
Space Separator 17182
 
13.9%
Other Punctuation 1845
 
1.5%
Decimal Number 803
 
0.6%
Dash Punctuation 533
 
0.4%
Other Letter 415
 
0.3%
Open Punctuation 411
 
0.3%
Close Punctuation 410
 
0.3%
Math Symbol 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11807
14.8%
o 8054
10.1%
a 7017
 
8.8%
n 6371
 
8.0%
i 5787
 
7.2%
r 5511
 
6.9%
t 5333
 
6.7%
s 3887
 
4.9%
l 3792
 
4.7%
h 3368
 
4.2%
Other values (56) 18981
23.8%
Uppercase Letter
ValueCountFrequency (%)
T 2279
 
10.3%
S 1702
 
7.7%
M 1530
 
6.9%
I 1473
 
6.7%
L 1449
 
6.5%
A 1415
 
6.4%
B 1136
 
5.1%
D 1086
 
4.9%
W 1052
 
4.8%
C 996
 
4.5%
Other values (27) 8024
36.2%
Other Letter
ValueCountFrequency (%)
י 67
16.1%
ל 45
10.8%
ה 39
 
9.4%
ו 32
 
7.7%
ב 24
 
5.8%
א 24
 
5.8%
ר 22
 
5.3%
ד 20
 
4.8%
נ 16
 
3.9%
ת 15
 
3.6%
Other values (24) 111
26.7%
Other Punctuation
ValueCountFrequency (%)
' 939
50.9%
. 307
 
16.6%
, 198
 
10.7%
/ 114
 
6.2%
: 101
 
5.5%
" 70
 
3.8%
? 41
 
2.2%
& 37
 
2.0%
! 23
 
1.2%
¿ 6
 
0.3%
Other values (5) 9
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 142
17.7%
2 140
17.4%
1 139
17.3%
9 91
11.3%
8 65
8.1%
4 56
 
7.0%
3 48
 
6.0%
5 47
 
5.9%
6 41
 
5.1%
7 34
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 532
99.8%
1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 403
98.1%
[ 8
 
1.9%
Close Punctuation
ValueCountFrequency (%)
) 402
98.0%
] 8
 
2.0%
Math Symbol
ValueCountFrequency (%)
= 1
50.0%
+ 1
50.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
50.0%
´ 1
50.0%
Space Separator
ValueCountFrequency (%)
17182
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102000
82.5%
Common 21190
 
17.1%
Hebrew 408
 
0.3%
Cyrillic 50
 
< 0.1%
Katakana 7
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11807
 
11.6%
o 8054
 
7.9%
a 7017
 
6.9%
n 6371
 
6.2%
i 5787
 
5.7%
r 5511
 
5.4%
t 5333
 
5.2%
s 3887
 
3.8%
l 3792
 
3.7%
h 3368
 
3.3%
Other values (71) 41073
40.3%
Common
ValueCountFrequency (%)
17182
81.1%
' 939
 
4.4%
- 532
 
2.5%
( 403
 
1.9%
) 402
 
1.9%
. 307
 
1.4%
, 198
 
0.9%
0 142
 
0.7%
2 140
 
0.7%
1 139
 
0.7%
Other values (28) 806
 
3.8%
Hebrew
ValueCountFrequency (%)
י 67
16.4%
ל 45
11.0%
ה 39
 
9.6%
ו 32
 
7.8%
ב 24
 
5.9%
א 24
 
5.9%
ר 22
 
5.4%
ד 20
 
4.9%
נ 16
 
3.9%
ת 15
 
3.7%
Other values (17) 104
25.5%
Cyrillic
ValueCountFrequency (%)
о 7
14.0%
с 5
 
10.0%
а 5
 
10.0%
н 4
 
8.0%
п 3
 
6.0%
к 3
 
6.0%
и 3
 
6.0%
ь 3
 
6.0%
В 2
 
4.0%
л 2
 
4.0%
Other values (12) 13
26.0%
Katakana
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122898
99.4%
Hebrew 408
 
0.3%
None 288
 
0.2%
Cyrillic 50
 
< 0.1%
Katakana 8
 
< 0.1%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17182
 
14.0%
e 11807
 
9.6%
o 8054
 
6.6%
a 7017
 
5.7%
n 6371
 
5.2%
i 5787
 
4.7%
r 5511
 
4.5%
t 5333
 
4.3%
s 3887
 
3.2%
l 3792
 
3.1%
Other values (74) 48157
39.2%
Hebrew
ValueCountFrequency (%)
י 67
16.4%
ל 45
11.0%
ה 39
 
9.6%
ו 32
 
7.8%
ב 24
 
5.9%
א 24
 
5.9%
ר 22
 
5.4%
ד 20
 
4.9%
נ 16
 
3.9%
ת 15
 
3.7%
Other values (17) 104
25.5%
None
ValueCountFrequency (%)
é 61
21.2%
á 31
10.8%
í 31
10.8%
ó 28
9.7%
ç 20
 
6.9%
ã 17
 
5.9%
ú 15
 
5.2%
ñ 15
 
5.2%
â 8
 
2.8%
ü 7
 
2.4%
Other values (21) 55
19.1%
Cyrillic
ValueCountFrequency (%)
о 7
14.0%
с 5
 
10.0%
а 5
 
10.0%
н 4
 
8.0%
п 3
 
6.0%
к 3
 
6.0%
и 3
 
6.0%
ь 3
 
6.0%
В 2
 
4.0%
л 2
 
4.0%
Other values (12) 13
26.0%
Punctuation
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

artist
Categorical

Distinct2021
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
The Cleaners From Venus
 
47
Nobuo Uematsu
 
46
Malcolm Arnold
 
45
Ricky Skaggs
 
41
Skinny Puppy
 
36
Other values (2016)
6693 

Length

Max length78
Median length46
Mean length12.808917
Min length1

Characters and Unicode

Total characters88484
Distinct characters90
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique968 ?
Unique (%)14.0%

Sample

1st rowBig Joe Williams
2nd rowThe Motels
3rd rowBéla Fleck
4th rowThe Pogues
5th rowJohn Schneider

Common Values

ValueCountFrequency (%)
The Cleaners From Venus 47
 
0.7%
Nobuo Uematsu 46
 
0.7%
Malcolm Arnold 45
 
0.7%
Ricky Skaggs 41
 
0.6%
Skinny Puppy 36
 
0.5%
Stan Getz 33
 
0.5%
Sisters of Mercy 33
 
0.5%
Éric Serra 32
 
0.5%
The Cramps 32
 
0.5%
Running Wild 32
 
0.5%
Other values (2011) 6531
94.5%

Length

2022-11-29T17:50:26.801999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 780
 
5.2%
262
 
1.7%
band 144
 
1.0%
john 110
 
0.7%
of 93
 
0.6%
and 73
 
0.5%
jackson 64
 
0.4%
billy 59
 
0.4%
michael 56
 
0.4%
los 53
 
0.4%
Other values (2792) 13321
88.7%

Most occurring characters

ValueCountFrequency (%)
e 8191
 
9.3%
8107
 
9.2%
a 6526
 
7.4%
n 5884
 
6.6%
o 5213
 
5.9%
r 5157
 
5.8%
i 5147
 
5.8%
l 3727
 
4.2%
s 3677
 
4.2%
t 3265
 
3.7%
Other values (80) 33590
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64386
72.8%
Uppercase Letter 15104
 
17.1%
Space Separator 8107
 
9.2%
Other Punctuation 643
 
0.7%
Decimal Number 153
 
0.2%
Dash Punctuation 53
 
0.1%
Close Punctuation 19
 
< 0.1%
Open Punctuation 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8191
12.7%
a 6526
10.1%
n 5884
 
9.1%
o 5213
 
8.1%
r 5157
 
8.0%
i 5147
 
8.0%
l 3727
 
5.8%
s 3677
 
5.7%
t 3265
 
5.1%
h 2521
 
3.9%
Other values (30) 15078
23.4%
Uppercase Letter
ValueCountFrequency (%)
T 1399
 
9.3%
S 1397
 
9.2%
B 1314
 
8.7%
C 1048
 
6.9%
M 978
 
6.5%
R 971
 
6.4%
J 863
 
5.7%
D 807
 
5.3%
A 755
 
5.0%
P 669
 
4.4%
Other values (18) 4903
32.5%
Decimal Number
ValueCountFrequency (%)
0 38
24.8%
2 31
20.3%
8 28
18.3%
3 16
10.5%
5 10
 
6.5%
4 10
 
6.5%
9 8
 
5.2%
7 6
 
3.9%
1 4
 
2.6%
6 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
& 262
40.7%
. 225
35.0%
' 81
 
12.6%
, 32
 
5.0%
/ 17
 
2.6%
" 14
 
2.2%
! 11
 
1.7%
? 1
 
0.2%
Space Separator
ValueCountFrequency (%)
8107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79490
89.8%
Common 8994
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8191
 
10.3%
a 6526
 
8.2%
n 5884
 
7.4%
o 5213
 
6.6%
r 5157
 
6.5%
i 5147
 
6.5%
l 3727
 
4.7%
s 3677
 
4.6%
t 3265
 
4.1%
h 2521
 
3.2%
Other values (58) 30182
38.0%
Common
ValueCountFrequency (%)
8107
90.1%
& 262
 
2.9%
. 225
 
2.5%
' 81
 
0.9%
- 53
 
0.6%
0 38
 
0.4%
, 32
 
0.4%
2 31
 
0.3%
8 28
 
0.3%
) 19
 
0.2%
Other values (12) 118
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88145
99.6%
None 339
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8191
 
9.3%
8107
 
9.2%
a 6526
 
7.4%
n 5884
 
6.7%
o 5213
 
5.9%
r 5157
 
5.9%
i 5147
 
5.8%
l 3727
 
4.2%
s 3677
 
4.2%
t 3265
 
3.7%
Other values (64) 33251
37.7%
None
ValueCountFrequency (%)
é 79
23.3%
í 54
15.9%
É 48
14.2%
ü 43
12.7%
á 26
 
7.7%
ã 22
 
6.5%
ú 15
 
4.4%
ó 14
 
4.1%
ä 13
 
3.8%
ô 9
 
2.7%
Other values (6) 16
 
4.7%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6898
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
spotify:track:3PzsbWSQdLCKDLxn7YZfkM
 
2
spotify:track:4XcUFQNTiX4IHmA4K51snP
 
2
spotify:track:6ArFGc1tVxYEhmGreRYoAi
 
2
spotify:track:35Z9SYT8AjvgvUag0H4iQt
 
2
spotify:track:7vvRkLPIvfjjmCIqNxBuEZ
 
2
Other values (6893)
6898 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters248688
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6888 ?
Unique (%)99.7%

Sample

1st rowspotify:track:1ZjN5X8LmUB67pWPgimW3B
2nd rowspotify:track:4fLIM0B1WwrLux9RdnMvze
3rd rowspotify:track:3DwlNfiCQSdj0GOxYkR9Rq
4th rowspotify:track:6JyYNPLalPgGa7XnclF5FO
5th rowspotify:track:6jJi8OXF5qaFdysB6sjWIT

Common Values

ValueCountFrequency (%)
spotify:track:3PzsbWSQdLCKDLxn7YZfkM 2
 
< 0.1%
spotify:track:4XcUFQNTiX4IHmA4K51snP 2
 
< 0.1%
spotify:track:6ArFGc1tVxYEhmGreRYoAi 2
 
< 0.1%
spotify:track:35Z9SYT8AjvgvUag0H4iQt 2
 
< 0.1%
spotify:track:7vvRkLPIvfjjmCIqNxBuEZ 2
 
< 0.1%
spotify:track:3dXSVFWK1s0PgtMrAifdDd 2
 
< 0.1%
spotify:track:0v9kGNjkKdQUdDoBIuiph4 2
 
< 0.1%
spotify:track:3uy0jtkM8QYVTsBazkli1x 2
 
< 0.1%
spotify:track:1gQPqkrPZ1RypZvSYEAygs 2
 
< 0.1%
spotify:track:3EgvmOhP3NQUHY7d6PDOUg 2
 
< 0.1%
Other values (6888) 6888
99.7%

Length

2022-11-29T17:50:26.896298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:3pzsbwsqdlckdlxn7yzfkm 2
 
< 0.1%
spotify:track:6arfgc1tvxyehmgreryoai 2
 
< 0.1%
spotify:track:35z9syt8ajvgvuag0h4iqt 2
 
< 0.1%
spotify:track:7vvrklpivfjjmciqnxbuez 2
 
< 0.1%
spotify:track:3dxsvfwk1s0pgtmraifddd 2
 
< 0.1%
spotify:track:0v9kgnjkkdquddobiuiph4 2
 
< 0.1%
spotify:track:3uy0jtkm8qyvtsbazkli1x 2
 
< 0.1%
spotify:track:1gqpqkrpz1rypzvsyeaygs 2
 
< 0.1%
spotify:track:3egvmohp3nquhy7d6pdoug 2
 
< 0.1%
spotify:track:4xcufqntix4ihma4k51snp 2
 
< 0.1%
Other values (6888) 6888
99.7%

Most occurring characters

ValueCountFrequency (%)
t 16190
 
6.5%
: 13816
 
5.6%
o 9286
 
3.7%
y 9284
 
3.7%
i 9281
 
3.7%
r 9281
 
3.7%
k 9275
 
3.7%
c 9262
 
3.7%
a 9254
 
3.7%
f 9233
 
3.7%
Other values (53) 144526
58.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 143988
57.9%
Uppercase Letter 60610
24.4%
Decimal Number 30274
 
12.2%
Other Punctuation 13816
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 16190
 
11.2%
o 9286
 
6.4%
y 9284
 
6.4%
i 9281
 
6.4%
r 9281
 
6.4%
k 9275
 
6.4%
c 9262
 
6.4%
a 9254
 
6.4%
f 9233
 
6.4%
s 9178
 
6.4%
Other values (16) 44464
30.9%
Uppercase Letter
ValueCountFrequency (%)
A 2425
 
4.0%
M 2384
 
3.9%
I 2381
 
3.9%
S 2379
 
3.9%
Z 2375
 
3.9%
F 2366
 
3.9%
H 2357
 
3.9%
E 2354
 
3.9%
Y 2343
 
3.9%
P 2342
 
3.9%
Other values (16) 36904
60.9%
Decimal Number
ValueCountFrequency (%)
1 3318
11.0%
0 3264
10.8%
4 3262
10.8%
3 3242
10.7%
5 3231
10.7%
2 3193
10.5%
6 3105
10.3%
7 2975
9.8%
8 2345
7.7%
9 2339
7.7%
Other Punctuation
ValueCountFrequency (%)
: 13816
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 204598
82.3%
Common 44090
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 16190
 
7.9%
o 9286
 
4.5%
y 9284
 
4.5%
i 9281
 
4.5%
r 9281
 
4.5%
k 9275
 
4.5%
c 9262
 
4.5%
a 9254
 
4.5%
f 9233
 
4.5%
s 9178
 
4.5%
Other values (42) 105074
51.4%
Common
ValueCountFrequency (%)
: 13816
31.3%
1 3318
 
7.5%
0 3264
 
7.4%
4 3262
 
7.4%
3 3242
 
7.4%
5 3231
 
7.3%
2 3193
 
7.2%
6 3105
 
7.0%
7 2975
 
6.7%
8 2345
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 248688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 16190
 
6.5%
: 13816
 
5.6%
o 9286
 
3.7%
y 9284
 
3.7%
i 9281
 
3.7%
r 9281
 
3.7%
k 9275
 
3.7%
c 9262
 
3.7%
a 9254
 
3.7%
f 9233
 
3.7%
Other values (53) 144526
58.1%

danceability
Real number (ℝ)

Distinct848
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56425819
Minimum0.0656
Maximum0.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:26.986123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0656
5-th percentile0.241
Q10.455
median0.582
Q30.69
95-th percentile0.822
Maximum0.988
Range0.9224
Interquartile range (IQR)0.235

Descriptive statistics

Standard deviation0.17308449
Coefficient of variation (CV)0.30674698
Kurtosis-0.24418724
Mean0.56425819
Median Absolute Deviation (MAD)0.116
Skewness-0.40359283
Sum3897.8956
Variance0.029958242
MonotonicityNot monotonic
2022-11-29T17:50:27.079955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54 28
 
0.4%
0.577 25
 
0.4%
0.622 24
 
0.3%
0.655 24
 
0.3%
0.61 24
 
0.3%
0.612 24
 
0.3%
0.62 23
 
0.3%
0.608 23
 
0.3%
0.716 23
 
0.3%
0.652 23
 
0.3%
Other values (838) 6667
96.5%
ValueCountFrequency (%)
0.0656 1
< 0.1%
0.0664 1
< 0.1%
0.0687 1
< 0.1%
0.0702 1
< 0.1%
0.0724 1
< 0.1%
0.0735 2
< 0.1%
0.0737 1
< 0.1%
0.0744 1
< 0.1%
0.0755 1
< 0.1%
0.0758 1
< 0.1%
ValueCountFrequency (%)
0.988 1
< 0.1%
0.98 1
< 0.1%
0.978 1
< 0.1%
0.97 1
< 0.1%
0.966 1
< 0.1%
0.963 1
< 0.1%
0.96 1
< 0.1%
0.959 1
< 0.1%
0.956 1
< 0.1%
0.955 2
< 0.1%

energy
Real number (ℝ)

Distinct1067
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60803705
Minimum0.000276
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:27.182341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.000276
5-th percentile0.164
Q10.436
median0.637
Q30.809
95-th percentile0.948
Maximum1
Range0.999724
Interquartile range (IQR)0.373

Descriptive statistics

Standard deviation0.24296181
Coefficient of variation (CV)0.39958389
Kurtosis-0.68506387
Mean0.60803705
Median Absolute Deviation (MAD)0.184
Skewness-0.43100596
Sum4200.3199
Variance0.05903044
MonotonicityNot monotonic
2022-11-29T17:50:27.282096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.751 20
 
0.3%
0.727 19
 
0.3%
0.939 18
 
0.3%
0.73 18
 
0.3%
0.873 18
 
0.3%
0.63 18
 
0.3%
0.64 18
 
0.3%
0.834 18
 
0.3%
0.722 17
 
0.2%
0.814 17
 
0.2%
Other values (1057) 6727
97.4%
ValueCountFrequency (%)
0.000276 1
< 0.1%
0.00154 1
< 0.1%
0.00247 1
< 0.1%
0.00323 1
< 0.1%
0.00395 1
< 0.1%
0.00407 1
< 0.1%
0.00489 1
< 0.1%
0.00516 1
< 0.1%
0.0059 1
< 0.1%
0.00648 1
< 0.1%
ValueCountFrequency (%)
1 4
0.1%
0.999 3
< 0.1%
0.998 3
< 0.1%
0.997 2
 
< 0.1%
0.996 5
0.1%
0.995 7
0.1%
0.994 5
0.1%
0.993 6
0.1%
0.992 5
0.1%
0.991 3
< 0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2333526
Minimum0
Maximum11
Zeros912
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:27.588432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5972464
Coefficient of variation (CV)0.68736939
Kurtosis-1.3155051
Mean5.2333526
Median Absolute Deviation (MAD)3
Skewness0.020174664
Sum36152
Variance12.940182
MonotonicityNot monotonic
2022-11-29T17:50:27.650181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 913
13.2%
0 912
13.2%
2 852
12.3%
7 819
11.9%
4 604
8.7%
5 576
8.3%
11 537
7.8%
1 480
6.9%
10 402
5.8%
6 312
 
4.5%
Other values (2) 501
7.3%
ValueCountFrequency (%)
0 912
13.2%
1 480
6.9%
2 852
12.3%
3 217
 
3.1%
4 604
8.7%
5 576
8.3%
6 312
 
4.5%
7 819
11.9%
8 284
 
4.1%
9 913
13.2%
ValueCountFrequency (%)
11 537
7.8%
10 402
5.8%
9 913
13.2%
8 284
 
4.1%
7 819
11.9%
6 312
 
4.5%
5 576
8.3%
4 604
8.7%
3 217
 
3.1%
2 852
12.3%

loudness
Real number (ℝ)

Distinct5512
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.041894
Minimum-39.94
Maximum-0.683
Zeros0
Zeros (%)0.0%
Negative6908
Negative (%)100.0%
Memory size54.1 KiB
2022-11-29T17:50:27.733789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-39.94
5-th percentile-19.054
Q1-13.451
median-10.662
Q3-7.64025
95-th percentile-4.7537
Maximum-0.683
Range39.257
Interquartile range (IQR)5.81075

Descriptive statistics

Standard deviation4.6811659
Coefficient of variation (CV)-0.42394591
Kurtosis3.0830612
Mean-11.041894
Median Absolute Deviation (MAD)2.9075
Skewness-1.1933351
Sum-76277.405
Variance21.913314
MonotonicityNot monotonic
2022-11-29T17:50:27.833783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8.539 5
 
0.1%
-9.15 5
 
0.1%
-8.78 5
 
0.1%
-14.118 5
 
0.1%
-8.548 5
 
0.1%
-12.746 4
 
0.1%
-12.948 4
 
0.1%
-7.262 4
 
0.1%
-3.947 4
 
0.1%
-6.293 4
 
0.1%
Other values (5502) 6863
99.3%
ValueCountFrequency (%)
-39.94 1
< 0.1%
-39.307 1
< 0.1%
-38.002 1
< 0.1%
-37.915 1
< 0.1%
-37.611 1
< 0.1%
-37.325 1
< 0.1%
-36.828 1
< 0.1%
-35.954 1
< 0.1%
-34.99 1
< 0.1%
-34.255 1
< 0.1%
ValueCountFrequency (%)
-0.683 1
< 0.1%
-2.1 1
< 0.1%
-2.236 1
< 0.1%
-2.237 1
< 0.1%
-2.256 1
< 0.1%
-2.258 1
< 0.1%
-2.291 1
< 0.1%
-2.304 1
< 0.1%
-2.305 1
< 0.1%
-2.337 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
1
4750 
0
2158 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 4750
68.8%
0 2158
31.2%

Length

2022-11-29T17:50:27.925550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:50:27.998950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4750
68.8%
0 2158
31.2%

Most occurring characters

ValueCountFrequency (%)
1 4750
68.8%
0 2158
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4750
68.8%
0 2158
31.2%

Most occurring scripts

ValueCountFrequency (%)
Common 6908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4750
68.8%
0 2158
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4750
68.8%
0 2158
31.2%

speechiness
Real number (ℝ)

Distinct948
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057906022
Minimum0.0223
Maximum0.903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:28.078410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0223
5-th percentile0.027235
Q10.0327
median0.0406
Q30.058
95-th percentile0.148
Maximum0.903
Range0.8807
Interquartile range (IQR)0.0253

Descriptive statistics

Standard deviation0.057767799
Coefficient of variation (CV)0.99761298
Kurtosis46.193559
Mean0.057906022
Median Absolute Deviation (MAD)0.0098
Skewness5.6042138
Sum400.0148
Variance0.0033371186
MonotonicityNot monotonic
2022-11-29T17:50:28.172555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0295 44
 
0.6%
0.033 38
 
0.6%
0.0324 36
 
0.5%
0.032 33
 
0.5%
0.0309 33
 
0.5%
0.0282 32
 
0.5%
0.0326 32
 
0.5%
0.0327 31
 
0.4%
0.0288 31
 
0.4%
0.0294 31
 
0.4%
Other values (938) 6567
95.1%
ValueCountFrequency (%)
0.0223 1
 
< 0.1%
0.0224 1
 
< 0.1%
0.0226 1
 
< 0.1%
0.0228 2
< 0.1%
0.0232 1
 
< 0.1%
0.0233 3
< 0.1%
0.0234 1
 
< 0.1%
0.0235 1
 
< 0.1%
0.0236 2
< 0.1%
0.0237 2
< 0.1%
ValueCountFrequency (%)
0.903 1
< 0.1%
0.823 1
< 0.1%
0.77 1
< 0.1%
0.738 1
< 0.1%
0.715 1
< 0.1%
0.706 1
< 0.1%
0.705 1
< 0.1%
0.676 1
< 0.1%
0.666 1
< 0.1%
0.641 1
< 0.1%

acousticness
Real number (ℝ)

Distinct2431
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29306681
Minimum1.36 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:28.272880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.36 × 10-6
5-th percentile0.0006391
Q10.033375
median0.172
Q30.499
95-th percentile0.915
Maximum0.996
Range0.99599864
Interquartile range (IQR)0.465625

Descriptive statistics

Standard deviation0.30295038
Coefficient of variation (CV)1.0337246
Kurtosis-0.52596967
Mean0.29306681
Median Absolute Deviation (MAD)0.162875
Skewness0.88308187
Sum2024.5056
Variance0.091778933
MonotonicityNot monotonic
2022-11-29T17:50:28.366539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.117 20
 
0.3%
0.102 19
 
0.3%
0.124 17
 
0.2%
0.181 17
 
0.2%
0.104 16
 
0.2%
0.12 15
 
0.2%
0.101 15
 
0.2%
0.109 14
 
0.2%
0.135 13
 
0.2%
0.108 13
 
0.2%
Other values (2421) 6749
97.7%
ValueCountFrequency (%)
1.36 × 10-61
< 0.1%
1.37 × 10-61
< 0.1%
2.1 × 10-61
< 0.1%
2.3 × 10-62
< 0.1%
2.35 × 10-62
< 0.1%
2.42 × 10-61
< 0.1%
2.82 × 10-61
< 0.1%
3.33 × 10-61
< 0.1%
3.6 × 10-61
< 0.1%
4.01 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 4
 
0.1%
0.995 12
0.2%
0.994 5
0.1%
0.993 4
 
0.1%
0.992 4
 
0.1%
0.991 4
 
0.1%
0.99 6
0.1%
0.989 8
0.1%
0.988 4
 
0.1%
0.987 2
 
< 0.1%

instrumentalness
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct3073
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13986283
Minimum0
Maximum1
Zeros1332
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:28.468437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.125 × 10-6
median0.000321
Q30.04525
95-th percentile0.88565
Maximum1
Range1
Interquartile range (IQR)0.045246875

Descriptive statistics

Standard deviation0.28814398
Coefficient of variation (CV)2.0601898
Kurtosis2.0705187
Mean0.13986283
Median Absolute Deviation (MAD)0.000321
Skewness1.9252816
Sum966.17245
Variance0.083026955
MonotonicityNot monotonic
2022-11-29T17:50:28.570343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1332
 
19.3%
0.907 10
 
0.1%
0.937 10
 
0.1%
0.843 9
 
0.1%
0.95 9
 
0.1%
0.853 8
 
0.1%
1.28 × 10-58
 
0.1%
0.865 8
 
0.1%
0.0109 7
 
0.1%
0.891 7
 
0.1%
Other values (3063) 5500
79.6%
ValueCountFrequency (%)
0 1332
19.3%
1.01 × 10-61
 
< 0.1%
1.02 × 10-62
 
< 0.1%
1.03 × 10-66
 
0.1%
1.05 × 10-62
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-64
 
0.1%
1.08 × 10-63
 
< 0.1%
1.09 × 10-63
 
< 0.1%
1.1 × 10-62
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.999 3
< 0.1%
0.993 2
< 0.1%
0.992 1
 
< 0.1%
0.99 2
< 0.1%
0.988 2
< 0.1%
0.986 2
< 0.1%
0.985 2
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct1384
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20100867
Minimum0.0186
Maximum0.997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:28.672340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0186
5-th percentile0.047635
Q10.0849
median0.131
Q30.266
95-th percentile0.62365
Maximum0.997
Range0.9784
Interquartile range (IQR)0.1811

Descriptive statistics

Standard deviation0.18166423
Coefficient of variation (CV)0.90376314
Kurtosis4.7237132
Mean0.20100867
Median Absolute Deviation (MAD)0.0616
Skewness2.1052781
Sum1388.5679
Variance0.033001892
MonotonicityNot monotonic
2022-11-29T17:50:28.772181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.104 51
 
0.7%
0.111 47
 
0.7%
0.105 46
 
0.7%
0.112 45
 
0.7%
0.108 45
 
0.7%
0.114 44
 
0.6%
0.106 44
 
0.6%
0.116 43
 
0.6%
0.107 41
 
0.6%
0.115 40
 
0.6%
Other values (1374) 6462
93.5%
ValueCountFrequency (%)
0.0186 1
< 0.1%
0.0205 1
< 0.1%
0.0212 1
< 0.1%
0.0223 2
< 0.1%
0.0226 1
< 0.1%
0.0234 1
< 0.1%
0.0235 2
< 0.1%
0.0237 1
< 0.1%
0.0239 1
< 0.1%
0.024 1
< 0.1%
ValueCountFrequency (%)
0.997 1
 
< 0.1%
0.993 1
 
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.987 1
 
< 0.1%
0.982 3
< 0.1%
0.981 1
 
< 0.1%
0.98 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 2
< 0.1%

valence
Real number (ℝ)

Distinct1110
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58792189
Minimum1 × 10-5
Maximum0.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:28.873862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.11
Q10.383
median0.622
Q30.811
95-th percentile0.958
Maximum0.99
Range0.98999
Interquartile range (IQR)0.428

Descriptive statistics

Standard deviation0.26360444
Coefficient of variation (CV)0.44836643
Kurtosis-0.92320644
Mean0.58792189
Median Absolute Deviation (MAD)0.211
Skewness-0.38011526
Sum4061.3644
Variance0.0694873
MonotonicityNot monotonic
2022-11-29T17:50:28.975690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.962 44
 
0.6%
0.961 44
 
0.6%
0.963 38
 
0.6%
0.964 29
 
0.4%
0.968 24
 
0.3%
0.965 22
 
0.3%
0.746 21
 
0.3%
0.647 21
 
0.3%
0.96 20
 
0.3%
0.805 18
 
0.3%
Other values (1100) 6627
95.9%
ValueCountFrequency (%)
1 × 10-510
0.1%
0.00516 1
 
< 0.1%
0.00953 1
 
< 0.1%
0.013 1
 
< 0.1%
0.017 1
 
< 0.1%
0.019 1
 
< 0.1%
0.0199 1
 
< 0.1%
0.0224 2
 
< 0.1%
0.0241 1
 
< 0.1%
0.0242 1
 
< 0.1%
ValueCountFrequency (%)
0.99 1
 
< 0.1%
0.984 2
 
< 0.1%
0.983 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 2
 
< 0.1%
0.979 3
< 0.1%
0.978 4
0.1%
0.977 7
0.1%
0.976 4
0.1%
0.975 7
0.1%

tempo
Real number (ℝ)

Distinct6641
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.64957
Minimum39.002
Maximum217.396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:29.069613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum39.002
5-th percentile78.98975
Q1102.6085
median119.546
Q3135.17525
95-th percentile172.28385
Maximum217.396
Range178.394
Interquartile range (IQR)32.56675

Descriptive statistics

Standard deviation27.108127
Coefficient of variation (CV)0.22468482
Kurtosis0.42285567
Mean120.64957
Median Absolute Deviation (MAD)16.2905
Skewness0.49937034
Sum833447.24
Variance734.85057
MonotonicityNot monotonic
2022-11-29T17:50:29.171162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.864 5
 
0.1%
119.941 4
 
0.1%
128.005 3
 
< 0.1%
200.713 3
 
< 0.1%
119.61 3
 
< 0.1%
122.004 3
 
< 0.1%
122.252 3
 
< 0.1%
125.257 3
 
< 0.1%
126.287 3
 
< 0.1%
109.756 3
 
< 0.1%
Other values (6631) 6875
99.5%
ValueCountFrequency (%)
39.002 1
< 0.1%
46.496 1
< 0.1%
48.169 1
< 0.1%
48.214 1
< 0.1%
49.423 1
< 0.1%
50.383 1
< 0.1%
51.569 1
< 0.1%
54.387 1
< 0.1%
54.64 1
< 0.1%
55.529 1
< 0.1%
ValueCountFrequency (%)
217.396 1
< 0.1%
214.121 1
< 0.1%
210.557 1
< 0.1%
209.758 1
< 0.1%
208.755 1
< 0.1%
208.62 1
< 0.1%
208.571 1
< 0.1%
207.859 1
< 0.1%
207.505 1
< 0.1%
207.197 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct5669
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254756.41
Minimum29514
Maximum2223827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:29.273653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum29514
5-th percentile136832.55
Q1204546.75
median241860
Q3287016.75
95-th percentile399711
Maximum2223827
Range2194313
Interquartile range (IQR)82470

Descriptive statistics

Standard deviation107321.35
Coefficient of variation (CV)0.42127045
Kurtosis55.502835
Mean254756.41
Median Absolute Deviation (MAD)41326.5
Skewness5.1084114
Sum1.7598573 × 109
Variance1.1517872 × 1010
MonotonicityNot monotonic
2022-11-29T17:50:29.372442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252307 6
 
0.1%
254000 6
 
0.1%
243067 5
 
0.1%
226267 5
 
0.1%
255000 5
 
0.1%
198000 5
 
0.1%
236733 5
 
0.1%
209000 5
 
0.1%
243533 4
 
0.1%
225333 4
 
0.1%
Other values (5659) 6858
99.3%
ValueCountFrequency (%)
29514 1
< 0.1%
30428 1
< 0.1%
31360 1
< 0.1%
31413 1
< 0.1%
31427 1
< 0.1%
31594 1
< 0.1%
34000 1
< 0.1%
34107 1
< 0.1%
34160 1
< 0.1%
34600 1
< 0.1%
ValueCountFrequency (%)
2223827 1
< 0.1%
1755307 1
< 0.1%
1713307 1
< 0.1%
1549933 1
< 0.1%
1496133 1
< 0.1%
1496053 1
< 0.1%
1448653 1
< 0.1%
1428827 1
< 0.1%
1400000 1
< 0.1%
1346027 1
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
4
6369 
3
 
430
5
 
62
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6908
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 6369
92.2%
3 430
 
6.2%
5 62
 
0.9%
1 47
 
0.7%

Length

2022-11-29T17:50:29.455332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:50:29.538194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 6369
92.2%
3 430
 
6.2%
5 62
 
0.9%
1 47
 
0.7%

Most occurring characters

ValueCountFrequency (%)
4 6369
92.2%
3 430
 
6.2%
5 62
 
0.9%
1 47
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6369
92.2%
3 430
 
6.2%
5 62
 
0.9%
1 47
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 6908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6369
92.2%
3 430
 
6.2%
5 62
 
0.9%
1 47
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6369
92.2%
3 430
 
6.2%
5 62
 
0.9%
1 47
 
0.7%

chorus_hit
Real number (ℝ)

Distinct6854
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.755172
Minimum0
Maximum433.182
Zeros21
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:29.617495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.60204
Q127.441758
median35.581815
Q347.318412
95-th percentile72.851111
Maximum433.182
Range433.182
Interquartile range (IQR)19.876655

Descriptive statistics

Standard deviation19.13636
Coefficient of variation (CV)0.48135524
Kurtosis34.052791
Mean39.755172
Median Absolute Deviation (MAD)9.54639
Skewness3.2326641
Sum274628.73
Variance366.20029
MonotonicityNot monotonic
2022-11-29T17:50:29.708529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
0.3%
19.88463 5
 
0.1%
25.59008 2
 
< 0.1%
47.86528 2
 
< 0.1%
49.49006 2
 
< 0.1%
23.16606 2
 
< 0.1%
33.34393 2
 
< 0.1%
24.17049 2
 
< 0.1%
55.2976 2
 
< 0.1%
49.23759 2
 
< 0.1%
Other values (6844) 6866
99.4%
ValueCountFrequency (%)
0 21
0.3%
5.28582 1
 
< 0.1%
5.33464 1
 
< 0.1%
5.51701 1
 
< 0.1%
6.0381 1
 
< 0.1%
8.13696 1
 
< 0.1%
8.27712 1
 
< 0.1%
8.30239 1
 
< 0.1%
8.32665 1
 
< 0.1%
8.56652 1
 
< 0.1%
ValueCountFrequency (%)
433.182 1
< 0.1%
235.61008 1
< 0.1%
220.48609 1
< 0.1%
196.20979 1
< 0.1%
182.01231 1
< 0.1%
179.42481 1
< 0.1%
175.56475 1
< 0.1%
172.91102 1
< 0.1%
158.85822 1
< 0.1%
158.53 1
< 0.1%

sections
Real number (ℝ)

Distinct51
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.277649
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.1 KiB
2022-11-29T17:50:29.807348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median11
Q313
95-th percentile18
Maximum73
Range72
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.472095
Coefficient of variation (CV)0.39654497
Kurtosis33.884694
Mean11.277649
Median Absolute Deviation (MAD)2
Skewness3.7076756
Sum77906
Variance19.999634
MonotonicityNot monotonic
2022-11-29T17:50:29.900174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 963
13.9%
10 887
12.8%
12 803
11.6%
9 785
11.4%
13 603
8.7%
8 602
8.7%
14 408
5.9%
7 374
 
5.4%
15 287
 
4.2%
16 209
 
3.0%
Other values (41) 987
14.3%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 18
 
0.3%
3 48
 
0.7%
4 86
 
1.2%
5 133
 
1.9%
6 204
 
3.0%
7 374
5.4%
8 602
8.7%
9 785
11.4%
10 887
12.8%
ValueCountFrequency (%)
73 2
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
63 2
< 0.1%
62 1
< 0.1%
55 2
< 0.1%
50 2
< 0.1%
46 2
< 0.1%
45 1
< 0.1%
44 1
< 0.1%

target
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.1 KiB
0
3454 
1
3454 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3454
50.0%
1 3454
50.0%

Length

2022-11-29T17:50:29.991004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:50:30.061633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3454
50.0%
1 3454
50.0%

Most occurring characters

ValueCountFrequency (%)
0 3454
50.0%
1 3454
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3454
50.0%
1 3454
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3454
50.0%
1 3454
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3454
50.0%
1 3454
50.0%

Interactions

2022-11-29T17:50:25.122387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:10.693988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.812719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.937869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:14.039882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.852919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.970712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.090067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.361634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.463937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.582686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.917213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.019405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.207227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:10.778637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.897367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.016025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:14.140176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.937557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.055374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.174746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.446271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.548592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.667152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.001719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.104278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.291725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:10.863217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.982020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.115917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:14.233939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.015711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.140033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.406592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.524414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.626745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.751806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.086064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.188946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.376020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:10.947886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.066277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.185111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:14.926250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.100052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.218187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.490946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.608772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.711418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.852102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.170751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.274001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.476312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.032528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.166588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.285242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.025574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.200326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.318659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.590968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.693297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.811437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.945874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.255411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.358291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.560958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.126289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.251253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.369910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.113924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.285148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.403010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.660012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.777974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.896109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.030544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.349171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.436445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.639145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.210932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.335895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.454567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.204274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.369320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.487877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.760306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.862652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.980949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.115213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.433824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.521102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.723788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.295580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.414077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.538794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.298757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.453989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.572542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.844949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.962607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.065627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.199840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.518183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.605762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.824087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.380272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.499096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.616936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.383261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.538653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.657221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.923102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.047262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.150282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.300108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.602766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.690283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.912424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.465092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.583613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.712312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.483063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.616795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.741876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.007446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.125415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.228424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.384803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.687107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.774690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:26.008919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.549612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.683900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.801488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.583338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.701475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.835645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.107749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.209931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.318183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.654101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.771784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.874965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:26.093761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.634289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.768549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.870548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.676022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.801413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:17.920502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.192276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.294598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.413486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.747864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.856420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:24.953098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:26.178411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:11.712425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:12.853228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:13.955225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:15.752639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:16.870900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:18.005205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:19.276954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:20.379250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:21.498147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:22.832536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:23.934574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:25.037742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-29T17:50:30.133006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T17:50:30.285155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T17:50:30.437745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T17:50:30.588459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T17:50:30.723125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T17:50:30.804767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T17:50:26.310134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T17:50:26.507969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
0Walking BluesBig Joe Williamsspotify:track:1ZjN5X8LmUB67pWPgimW3B0.5090.2776-14.32310.04950.8270000.0020600.07560.640101.157161893468.4653070
1Suddenly Last SummerThe Motelsspotify:track:4fLIM0B1WwrLux9RdnMvze0.7160.7532-5.68210.02860.1620000.0306000.08310.561120.141222000457.71583111
2SanctuaryBéla Fleckspotify:track:3DwlNfiCQSdj0GOxYkR9Rq0.3600.5425-13.88510.03390.3680000.1650000.11600.803116.831444907430.34574170
3The Wild RoverThe Poguesspotify:track:6JyYNPLalPgGa7XnclF5FO0.6560.5127-11.87210.02900.5850000.0000000.07200.88097.500157893350.9702270
4In The Driver's SeatJohn Schneiderspotify:track:6jJi8OXF5qaFdysB6sjWIT0.6420.8892-5.62000.04940.3750000.0000000.18000.764163.351162293433.6205371
5Slow KillFields Of The Nephilimspotify:track:3AKzRH32S4Jc5Ge4RFp5WG0.2960.5474-15.11500.03270.0002910.0136000.37200.490148.775224360453.02555110
6Young Wild And FreeBrighton Rockspotify:track:7EBpncUwlHjLhQTetSLb9O0.4540.7342-15.55910.04100.1240000.0000140.09900.460135.527225560436.52688110
7HolidayThe Other Onesspotify:track:3f1rbdXIbz36QZ8xU7wt2i0.4870.85311-10.16500.03430.0277000.0073700.13600.80283.593213427415.23401101
8Answering MachineRupert Holmesspotify:track:5Qo14bQqTK9iGbf2g6JUjL0.7750.4880-15.04410.12300.2400000.0000000.03680.96181.158215200428.6564391
9Crystal BallYngwie Malmsteenspotify:track:6C3BPDXuHFRbCWsTk4eNPU0.4060.7778-11.49810.05900.0870000.0000160.19600.456131.109295507441.44096120
trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
6898Der fröhliche GesellePeter Maffayspotify:track:2CmeFJnFAuDLRKgMEgdE7Y0.6340.4015-15.20500.06270.7680000.0005750.13700.664107.011213400436.65216100
6899In A LifetimeClannadspotify:track:4Ye4VJlktPTaQ1mK8s6vC00.4440.3832-15.97700.03460.6610000.0005200.06560.297133.935191827456.2440170
6900Cold HeartedPaula Abdulspotify:track:6jrp8qBMJO6vhAeYVAsdk90.8790.6797-8.59000.04210.1490000.0087100.07800.724122.380231400459.57122111
6901The Hunter's MoonHarvey Reidspotify:track:1XUGKhR63kzlEUqZM5xIgT0.3300.2127-11.26810.03370.7640000.7650000.11500.352179.156465027432.42416240
6902Some Guys Have All The LuckRod Stewartspotify:track:53Eqyg5eFqyuUCaSwozq4f0.7510.6172-15.10310.03900.1360000.0000220.05040.907126.300274307432.22649141
6903Bachke Rehna Re Baba - Pukar / Soundtrack VersionKishore Kumarspotify:track:2wK3g7TTQa9AJMKcuYKbon0.4430.5897-8.08510.03920.5520000.0000000.14100.96397.401384360431.61135230
6904Tiempos MetálicosV8spotify:track:3QaUanfuOovKQ5yNgGOSsz0.2080.7509-12.94510.10800.0001340.0000020.06210.39483.467154827423.7178080
6905JoyTeddy Pendergrassspotify:track:2Qv5EUATFNebcFGq3rN8O60.7390.7294-9.69400.05720.2490000.0076800.06900.884103.339375652431.31730131
6906I Wanna Be A CowboyBoys Don't Cryspotify:track:38mEFmht9K7UcpHvS9vtJG0.7490.6260-12.42610.03930.0115000.5670000.05580.770142.565366133427.71571181
6907Hot In The CityBilly Idolspotify:track:5LeDMHIZ5YDZ2b1VOcYVcG0.6600.8975-5.55810.02570.0031600.0000230.11600.670111.193219133451.4039991